In the Materials Horizons Emerging Investigator Series, Dr. Eunho Lee from SeoulTech talks about his recent study on novel organic semiconductor materials with glycol side chains that enhance artificial synapses by shifting from surface-limited to bulk-mediated ion transport mechanisms. The research demonstrates how molecular design of conjugated polymers can improve synaptic performance in neuromorphic computing devices, offering pathways toward brain-inspired artificial intelligence hardware.

Embedded systems such as Internet of Things (IoT) devices and single-board computers require careful memory allocation management. In a recent breakthrough, SeoulTech researchers have proposed LWMalloc, a lightweight dynamic memory allocator designed for resource-constrained environments like IoT devices and embedded systems. The research demonstrates up to 53% faster execution times and 23% lower memory usage compared to Linux's default allocator, while requiring only 530 lines of code versus ptmalloc's 4,838 lines.

Researchers developed a deep learning-based multimodal prognostic model that shows strong potential to improve disease-free survival prediction and enable personalized treatment in locally advanced cervical cancer.

In the quest for sustainable and efficient water treatment solutions, a new study titled "Enhanced Machine Learning Prediction of Biochar Adsorption for Dyes: Parameter Optimization and Experimental Validation" is making significant strides. This research leverages the power of machine learning to optimize the adsorption capabilities of biochar for dye removal, offering a promising approach to tackling water pollution.